ArticleRespiratory research2023
Prediction of the number of asthma patients using environmental factors based on deep learning algorithms.
Article in Respiratory research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 16 citations in OpenAlex.
- Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures.Allergy · 2026Review
- Climate change, environmental exposures, and pediatric asthma exacerbations: air pollution, temperature, and relative humidity.Pediatric research · 2026Review
- Hybrid deep learning and YAMNet features for asthma diagnosis from respiratory sounds.Scientific reports · 2026Article
- Forecasting influenza activity based on internet retrieval behavior and meteorological data in four megacities of China.BMC infectious diseases · 2026Article
- Broad Applications of Distributed Lag Non-Linear Model in Public Health: A Comprehensive Review.GeoHealth · 2025Review
- Predicting hospital outpatient volume using XGBoost: a machine learning approach.Scientific reports · 2025Article
- Application and research progress of artificial intelligence in allergic diseases.International journal of medical sciences · 2025Review
- Predicting Asthma Exacerbation Risk in the Adult South Korean Population Using Integrated Health Data and Machine Learning Models.Journal of asthma and allergy · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundAir pollution, weather, pollen, and influenza are typical aggravating factors for asthma. Previous studies have identified risk factors using regression-based and ensemble models. However, studies that consider complex relationships and interactions among these factors have yet to be conducted. Although deep learning algorithms can address this problem, further research on modeling and interpreting the results is warranted.
methodsIn this study, from 2015 to 2019, information about air pollutants, weather conditions, pollen, and influenza were utilized to predict the number of emergency room patients and outpatients with asthma using recurrent neural network, long short-term memory (LSTM), and gated recurrent unit models. The relative importance of the environmental factors in asthma exacerbation was quantified through a feature importance analysis.
resultsWe found that LSTM was the best algorithm for modeling patients with asthma. Our results demonstrated that influenza, temperature, PM
conclusionLSTM is an excellent algorithm for modeling complex epidemiological relationships, encompassing nonlinearity, lagged responses, and interactions. Our study findings can guide policymakers in their efforts to understand the environmental factors of asthma exacerbation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.